Long only 1/n portfolio

import pandas as pd
pd.options.plotting.backend = "plotly"

import yfinance as yf

from cvx.simulator.builder import builder
from cvx.simulator.grid import resample_index

data = yf.download(tickers = "SPY AAPL GOOG MSFT",  # list of tickers
                   period = "10y",                   # time period
                   interval = "1d",                 # trading interval
                   prepost = False,                 # download pre/post market hours data?
                   repair = True)                   # repair obvious price errors e.g. 100x?

[                       0%%                      ]

[**********************50%%                      ]  2 of 4 completed

[**********************75%%**********            ]  3 of 4 completed

[*********************100%%**********************]  4 of 4 completed

prices = data["Adj Close"]

capital = 1e6

b = builder(prices=prices, initial_cash=capital)

for time, state in b:
    # each day we invest a quarter of the capital in the assets
    b[time[-1]] = 0.25 * state.nav / state.prices

portfolio = b.build()
portfolio.profit.cumsum().plot()

portfolio.nav.plot()

Rebalancing

Usually we would not execute on a daily basis but rather rebalance every week, month or quarter. There are two approaches to deal with this problem in cvxsimulator.

• Resample the existing daily portfolio (helpful to see effect of your hesitated trading)

• Trade only on days that are within a predefined grid (most flexible if you have a rather irregular grid)

Resample an existing portfolio

portfolio_resampled = portfolio.resample(rule="M")

frame = pd.DataFrame({"original": portfolio.nav, "monthly": portfolio_resampled.nav})
frame

	original	monthly
Date
2013-11-11	1.000000e+06	1.000000e+06
2013-11-12	9.987197e+05	9.987197e+05
2013-11-13	1.011473e+06	1.011459e+06
2013-11-14	1.016140e+06	1.016092e+06
2013-11-15	1.014108e+06	1.014055e+06
...	...	...
2023-11-03	7.114728e+06	7.095482e+06
2023-11-06	7.178338e+06	7.158943e+06
2023-11-07	7.242482e+06	7.223076e+06
2023-11-08	7.279633e+06	7.260282e+06
2023-11-09	7.226646e+06	7.207363e+06

2517 rows × 2 columns

print(portfolio_resampled.stocks)

                    AAPL          GOOG         MSFT          SPY
Date                                                            
2013-11-11  15362.798310   9932.299684  7941.947293  1691.555787
2013-11-12  15362.798310   9932.299684  7941.947293  1691.555787
2013-11-13  15362.798310   9932.299684  7941.947293  1691.555787
2013-11-14  15362.798310   9932.299684  7941.947293  1691.555787
2013-11-15  15362.798310   9932.299684  7941.947293  1691.555787
...                  ...           ...          ...          ...
2023-11-03  10010.306388  13651.273957  5032.198624  4120.316565
2023-11-06  10010.306388  13651.273957  5032.198624  4120.316565
2023-11-07  10010.306388  13651.273957  5032.198624  4120.316565
2023-11-08  10010.306388  13651.273957  5032.198624  4120.316565
2023-11-09  10010.306388  13651.273957  5032.198624  4120.316565

[2517 rows x 4 columns]

# almost hard to see that difference between the original and resampled portfolio
frame.plot()

# number of shares traded
portfolio_resampled.trades_stocks.iloc[1:].plot()

Trade only days in predefined grid

b = builder(prices=prices, initial_cash=capital)

# define a grid
grid = resample_index(prices.index, rule="M")

for time, state in b:
    # each day we invest a quarter of the capital in the assets
    if time[-1] in grid:
        b[time[-1]] = 0.25 * state.nav / state.prices
    else:
        # forward fill an existing position
        b[time[-1]] = b[time[-2]]
        
portfolio = b.build()

portfolio.nav.plot()

# Trading only once a month can lead to days where 150k had to be reallocated
portfolio.turnover.iloc[1:].plot()

Why not resampling the prices?

I don’t believe in bringing the prices to a monthly grid. This would render it hard to construct signals given the sparse grid. We stay on a daily grid and trade once a month.